Keynote - AI-Powered Search: Navigating the Evolving Lexicon of Information Retrieval

The field of information retrieval is evolving at a rapid pace, becoming a cornerstone of the generative AI space and absorbing different, yet familiar, techniques and terminology from previously tangential areas of AI world. Search engines are becoming vector databases, and vector databases (and even traditional SQL and NoSQL databases) are becoming search engines. In the process, we’re embracing new terminology like RAG, bi-encoders, cross-encoders, multimodal search, hybrid search, dense/sparse vectors and representations, contextualized late interaction, and quantization. But are these all new concepts, and if not, how do we mentally compare them to previous tried-and-true techniques like learning to rank (vs cross-encoders), lexical search (vs sparse vector search), semantic search (vs search over embeddings), collaborative filtering (vs latent behaviors embeddings), approximate nearest neighbors (vs quantization), knowledge graphs (vs foundation models), and dense vector search (vs bi-encoders)? Pulling from his experience writing (and frequently updating!) the newly-released book AI-Powered Search, Trey Grainger will provide a survey of the evolving landscape of search and relevance, highlighting how our traditional search toolbox and terminology are expanding in exciting ways and discussing what’s incoming on the frontier of search and AI.

Trey Grainger

Searchkernel

Trey Grainger is lead author of the _AI-Powered Search_ book (Manning 2024) and the Founder of Searchkernel, a software consultancy building the next generation of AI-powered search. He previously served as CTO of Presearch, a decentralized web search engine, and as Chief Algorithms Officer and SVP of Engineering at Lucidworks, an AI-powered search company whose search technology powers hundreds of the world’s leading organizations. He is also co-author of _Solr in Action_. Trey has 17 years of experience in search and data science, including significant work developing semantic search, personalization and recommendation systems, and building self-learning search platforms leveraging content and behavior-based reflected intelligence. This work resulted in the publication of dozens of research papers, journal articles, conference presentations, and books focused on intelligent search systems.